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Monitoring poverty reduction requires frequent microdata on household welfare that can be compared over time. Such data are unavailable in many countries, given limited statistical capacity, shocks that prevent data collection, and regular improvements to survey methodology. This paper demonstrates how jointly deploying backcasting and survey-to-survey imputations can help to overcome this in a setting where estimating a poverty trend is badly needed, given the scale of the poverty-reduction challenge, but where survey-to-survey imputations are more likely to succeed and can be directly tested. In Nigeria, the most recent official survey that can be used to construct an imputation model was collected through the same methodology and in the same year as the target survey. This data landscape could arise in other settings where the methodology for smaller, interstitial surveys is updated more quickly than for larger, official consumption surveys. Naively comparing Nigeria's last two official consumption surveys would suggest that the poverty rate fell by 17 percentage points between 2009 and 2019. Yet the methods presented in this paper both suggest a much smaller reduction in poverty of between 3 and 7 percentage points, echoing Nigeria's performance on nonmonetary welfare indicators over the same period. The paper therefore provides guidance on when and how backcasting and survey-to-survey imputation techniques can be most valuable for monitoring poverty reduction. [ABSTRACT FROM AUTHOR]

The Food Insecurity Experience Scale (FIES), which combines three food-access dimensions into a single indicator, is rapidly being incorporated into national statistical systems. However, there is no prediction about how one of the incorporated dimensions—subjective experiences associated with food insecurity—overlaps with poor food consumption. Using data from West Africa, this study illustrates that in 4 out of 10 countries, there is a similar prevalence of food insecurity according to the FIES among segments of the population that are likely undernourished and segments that are likely not undernourished. And in 5 out of 10 countries, there is a relatively large prevalence of food insecurity according to the FIES in the segments of the population that are least likely to be undernourished. Combined, the results offer guidance to policymakers when choosing food-access indicators and illustrate the importance of using the FIES along with other food-access measures. [ABSTRACT FROM AUTHOR]

Given the complexity of measuring resilience, development practitioners are constantly seeking observable proxies that can help target resilience-building initiatives. Evidence suggests that female-headed and male-headed households differ in terms of their vulnerability and resilience, so household head gender may provide policymakers with important information about how best to target their interventions. However, the extent to which these gender differences can be explained by other observable characteristics, such as education and household demographics, remains an open question. Using an index of resilience employed by a large non-governmental organization (Oxfam GB), we provide evidence on this question by comparing the resilience of female- and male-headed households interviewed in a series of 16 evaluations of rural development projects carried out in 12 countries across Africa, Asia, and Latin America. We find that there is a statistically significant difference between female- and male-headed households, on average, in terms of their measured resilience, and that only just over half of this difference can be explained by observable characteristics. However, since the size of this difference is small, using information on household head gender does not significantly improve the accuracy of targeting methods, such as proxy means tests, that aim to identify households for resilience-building initiatives. [ABSTRACT FROM AUTHOR]

In many urban African labour markets, women outnumber men in self-employment, even though gender earnings gaps are larger for the self-employed than the wage-employed. In this paper, we suggest an explanation for this pattern using a search and matching model that allows for individual heterogeneity and participation in both self- and wage-employment as well as discrimination against women in the wage sector. We show that discrimination in wage-employment can generate gender earnings gaps across all sectors of the economy, even if the underlying ability distributions for women and men are identical. This result arises because discrimination creates extra frictions for women, making it harder for them to select occupations according to their comparative advantage. Moreover, the earnings gaps that arise turn out to be larger for the self-employed, even though it is not the self-employment sector that is characterised by discrimination. Policy makers may therefore need to target the wage sector in order to address gender inequality for the self-employed. [ABSTRACT FROM AUTHOR]